AI Engineer

The role requires deep expertise in LLM systems, agentic AI, advanced ML infrastructure, backend engineering, distributed systems, and cloud platforms , with proven ownership of complex, cross-cutting AI systems spanning multiple teams or products. Experience building RAG architectures using vector search, Azure OpenAI, Amazon Bedrock, Gemini , and enterprise knowledge retrieval solutions.

Request ID: 110307-1
Title: AI Engineer
Location: Phoenix, AZ (Onsite)
Duration: 6 months
Pay Range: $40 - $45/Hour on W2/C2C (All inclusive)

JOB DESCRIPTION:
We are seeking an experienced AI Engineer with 9+ years of experience building large-scale distributed systems and driving enterprise AI transformations. The ideal candidate will bridge complex technical architectures with high-level business objectives while establishing robust Agile AI governance.
The role requires deep expertise in LLM systems, agentic AI, advanced ML infrastructure, backend engineering, distributed systems, and cloud platforms, with proven ownership of complex, cross-cutting AI systems spanning multiple teams or products.

Required Experience & Technical Skills:
9+ years of experience spearheading enterprise AI transformations and building large-scale distributed systems.
Strong experience with LLM systems, agentic workflows, or advanced ML infrastructure
Deep technical expertise in Python and Machine Learning.
Recent hands-on experience with Node.js, JavaScript, and TypeScript.
Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure.
Proven ownership of complex, cross-cutting agentic systems spanning multiple teams or products.

• Deep experience across the agentic AI stack, including:
  • Planning
  • Tool use
  • Memory
  • Evaluation
  • Autonomy boundaries
  • State management
• Experience designing production-ready Large Language Model (LLM) applications and multi-agent orchestration systems.
• Expertise in advanced GenAI workflows, including:
  • Retrieval-Augmented Generation (RAG)
  • Graph Knowledge Bases
  • Fine-tuning
  • Function-calling agents
  • Enterprise knowledge retrieval
• Experience designing enterprise agentic AI systems using frameworks and technologies such as:
  • LangGraph
  • LangChain
  • CrewAI
  • AutoGen
  • MCP
• Experience building RAG architectures using vector search, Azure OpenAI, Amazon Bedrock, Gemini, and enterprise knowledge retrieval solutions.
• Proficiency in LangChain and LangGraph for building context-aware applications.
• Experience optimizing vector embeddings, inference latency, and AI application performance, including Redis caching.
• Strong experience with Node.js backend services and APIs.
• Experience building TypeScript applications and AI-powered applications.
• Experience integrating Node.js with:
  • PostgreSQL
  • MongoDB
  • GraphQL
  • REST APIs
  • Next.js
• Strong experience with REST and gRPC APIs and services.
• Experience with Python and Go.
• Experience in workflow engines, asynchronous processing, queues, and streaming systems.
• Experience with distributed, event-driven architectures, including Apache Kafka.
• Strong experience building large-scale AI/ML platforms across AWS, Azure, and/or GCP.
• Experience with Kubernetes deployments and scalable cloud infrastructure.
• Experience with:
  • Airflow
  • MLflow
  • Kubeflow
• Experience integrating commercial and open-source LLMs into agentic workflows.
• Experience with agent and orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI, with strong judgment regarding when to use frameworks versus lightweight custom primitives.
• Experience with PyTorch and the Hugging Face ecosystem, including embeddings, fine-tuning, and inference tooling.
• Some exposure to TensorFlow.
• Strong schema validation and state management practices using tools such as Pydantic (Python) and Zod (TypeScript).

Agentic AI & LLM Expertise
The candidate should demonstrate hands-on experience designing and operating production-grade agentic AI systems covering:

• Agent frameworks and orchestration layers
• Planning and reasoning workflows
• Tool calling and function execution
• Agent memory strategies
• Retrieval and grounding pipelines
• RAG architectures
• LLM infrastructure, inference, and model gateways
• Evaluation and observability
• Safety tooling for autonomous systems
• Fine-tuning and model optimization
• Inference pipelines
• Context-aware AI applications
• Skill-based agentic workflows
• Multi-agent orchestration

Cloud, Infrastructure & Distributed Systems

• AWS and/or GCP cloud infrastructure experience.
• Experience with Azure and enterprise AI services is highly desirable.
• Kubernetes-based deployments and scalable infrastructure.
• Distributed and event-driven architectures.
• Kafka-based streaming pipelines.
• Airflow-based orchestration.
• MLflow/Kubeflow-based ML workflows.
• Experience designing reliable, scalable, and production-ready AI infrastructure.
• Strong understanding of scalability, reliability, security, observability, and cost considerations.

Backend & Application Engineering

• Node.js, JavaScript, and TypeScript.
• Python and Go.
• REST and gRPC APIs.
• PostgreSQL and MongoDB.
• GraphQL and Next.js.
• Async processing, queues, workflow engines, and streaming systems.
• Strong API, backend, data pipeline, and application architecture fundamentals.

Roles & Responsibilities:

• Drive technical direction for agentic AI initiatives, influencing architecture patterns, autonomy boundaries, and system design.
• Design, build, deploy, and operate production-grade agentic AI systems used across multiple products.
• Own and evolve shared agentic AI capabilities, including:

• Agent frameworks and orchestration layers
• Planning, tool use, and memory strategies
• Retrieval and grounding/RAG pipelines
• LLM infrastructure, inference, and model gateways
• Evaluation, observability, and safety tooling

Lead technical design reviews and help teams navigate tradeoffs involving:

• Autonomy
• Safety
• Reliability
• Scalability
• Performance
• Cost

• Partner across teams to deliver complex, cross-cutting agentic AI initiatives from concept through production.
• Translate ambiguous business and technical problems into reliable, autonomous systems that can be shipped and operated in production.
• Evaluate emerging models, techniques, and agentic patterns and translate them into practical enterprise solutions.
• Establish and maintain robust Agile AI governance practices.
• Influence technical direction and align teams without formal authority.
• Drive architecture and engineering best practices across AI products and platforms.
• Ensure AI systems are production-ready, observable, scalable, reliable, and maintainable.

Preferred / Desirable Qualifications:

• Experience building agentic systems in fintech or other regulated industries.
• Experience as a founding engineer or early technical leader in AI-driven products.
• Demonstrated success delivering technically complex autonomous systems that customers actively rely on.
• Meaningful contributions to open-source AI or agentic frameworks.
• Familiarity with fine-tuning, model optimization, and inference pipelines.
• Strong understanding of enterprise AI governance and responsible AI practices.

Company Benefits & Culture
• Opportunity to work with a dynamic team in a fast-paced environment
• Exposure to cutting-edge technologies and methodologies
• Supportive and collaborative work culture

For immediate consideration please click APPLY to begin the screening process with Alex.